R&D Data Sharing in New Product Development

• 成果类型: Article
• 署名作者: Chen, Zhi; Keppo, Jussi
• 署名单位: National University of Singapore; National University of Singapore
• 刊物名称: M&SOM-MANUFACTURING & SERVICE OPERATIONS MANAGEMENT
• ISSN/ISSBN: 1523-4614
• DOI: 10.1287/msom.2023.0463
• 发表日期: 2025
• 关键词: Innovation Contest; Data sharing; new product development; Artificial intelligence

摘要

Problem definition: Many innovations today are data driven. To improve algorithms of these products, firms make substantial investments in data collection. However, the data are limited for an individual firm, which caps the benefits of the algorithms. Data sharing among the firms can help alleviate this problem, but firms may be reluctant to share their proprietary data, because doing so may result in a loss of competitive advantage. Motivated by this tension, we ask the following two questions. First, when do firms voluntarily share their data and when are they reluctant to do so? Second, how does voluntary data sharing compare with the corresponding centralized data sharing (as if a government were to decide the level of data sharing to promote innovation)? Methodology/ results: Using a game-theoretic model, we identify two key factors that determine answers to the questions: (i) the relationship between firms' data sets and (ii) the degree uncertainty associated with the innovation. We find that firms voluntarily share data their data sets are complements or if the uncertainty is high. Moreover, relative to the centralized data sharing, firms voluntarily share too little (respectively, much) data when their data sets are complements (respectively, redundant) and the uncertainty is moderate (respectively, high). Managerial implications: Our findings provide plausible explanations on data-sharing practices, for example, why firms in the autonomous vehicle industry voluntarily share their proprietary data. We also shed light on the antitrust issues associated with data sharing, where too much voluntary data sharing can reduce firms' data collection incentives and stifle competition. Moreover, if the government considers subsidizing firms' data collection efforts to accelerate innovation, cost subsidies are particularly effective when paired with mandatory data-sharing regulations. The government should exercise caution under voluntary data sharing because higher subsidies may not necessarily lead to higher innovation.